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Using COVID-19 As A Learning Experience For Students

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What do you do when all your students have gone home and are looking for ways to have a meaningful impact while sheltering in place? For Dr. Pablo Rivas, an Assistant Professor of Computer Science at Marist College in Poughkeepsie, New York, the answer was to create a tool that would take data of COVID-19 infections and enable students to both enter data and modify its algorithms to provide predictions on the virus and its spread in their country, state or county. You can view the tool at https://www.rivas.ai/covid-19/.

Dr. Rivas had a class of Seniors majoring in data science, and former machine learning students, who were sent home due to the move to virtual classes for the rest of the semester. To keep them engaged and enable them to use their skills he created on his website a basic dashboard for tracking and forecasting COVID-19. Students are encouraged to continue to replicate the process, add local data, and to expand the data to include data information about cases and deaths in terms of ethnicities, age and economic status. Students can not only replicate the dashboard, but also modify the algorithms, re-training the models as new data is discovered.

“I wanted my students to be inspired and find ways to engage with this project for their local area. It will take time but they can modify the algorithm as richer data sources become available. This will enable them to make their own predictions on the impact of Covid-19. This will also teach them to ask questions, to seek out new data sources,” said Pablo.

Pablo uses the following Technology Stack:

  • Google Colaboratory and Jupyter Notebooks

  • TensorFlow/Keras and Scikit Learn on Python

  • Google Cloud

Dr. Pablo Rivas came to the US in 2008 from Mexico. Prior to joining Marist College he worked in industry for a decade as a software engineer before becoming an academic. He is a Senior Member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center, and at Baylor University performing post-doctoral research and teaching. He is considered an ally of women in technology, a deep learning evangelist, machine learning ethicist, and is a proponent of the democratization of machine learning and artificial intelligence in general. His research and teaching is on machine learning, including deep learning, theory and algorithms, and the societal implications of artificial intelligence. He obtained his PhD from The University of Texas at El Paso in 2011. In the Fall Pablo will be returning to Baylor University to join the Computer Science faculty.